Service

LLM integration & tuning

We embed language models inside your systems and tune them on your data, so outputs hit your accuracy bar, your tone, and your rules in production, not just in a demo.

Book a discovery call Evidence-based model choice · Measured with a test set · Guardrails on output

Who it's for

This is for small and mid-sized businesses that have seen what a model can do in a demo and now need it dependable in production. It suits teams who care less about which model is fashionable and more about output that clears a defined accuracy bar and stays within their rules.

How it works

How a model gets to production

We start with the cheapest approach that clears your quality bar and only escalate if the results genuinely require it, measuring every step of the way.

Step 1

Integrate

We wire a strong existing model into your applications through clean, well-scoped interfaces you can maintain, rather than a black box.

Step 2

Tune to your domain

We adapt behaviour to your terminology, tone, and specifics using prompt design, context, and your own examples, so output sounds like you.

Step 3

Evaluate

We build a test set from your real cases and score output against it, so quality is a number you can track, not a feeling from a demo.

Step 4

Guard and ship

We add guardrails and fallbacks that catch unsafe or off-target responses before a user sees them, then ship into production.

On model choice:we keep the integration model-agnostic where possible, so you can switch as prices and capabilities change rather than locking to one vendor.

What you get

Each item below is built for production use, where measurement replaces guesswork.

Maintainable integration

Language models wired into your applications through clean interfaces your team can keep running and extend.

Tuned behaviour

Model output adapted to your domain, terminology, and tone using your own examples, so it gets the specifics right.

Prompts and tools

Engineered prompts and tool definitions for each target task, making the model reliable rather than merely plausible.

Evaluation suite

A test set that scores accuracy against real examples, so you can prove quality instead of trusting a single demo.

Guardrails and fallbacks

Safety checks that catch and contain unsafe or wrong output before it reaches a user.

Outcomes

What changes after

Model output matches your accuracy bar, your terminology, and your tone.

You can measure quality with a test set instead of trusting a demo.

Unsafe or off-target responses are caught before they reach a user.

Not sure tuning is what you need?

A short call is enough to tell whether prompt work, tuning, or a different model gets you to your accuracy bar.

Book a discovery call

6 mo

To CJIS compliance

100%

Data isolation

Case study

A compliant AI assistant for law enforcement agencies

For a public-safety platform, Evertech made a model give law enforcement compliant, grounded answers under CJIS rules with complete data isolation, the kind of production discipline this service brings to any LLM.

Read the case study

FAQ

Questions buyers ask

Do we need to train our own model?

Rarely. Most needs are met by integrating a strong existing model and tuning its behaviour with prompts, context, and your own examples. Full training is expensive and seldom justified. Evertech starts with the cheapest approach that hits your accuracy bar and only escalates if the results genuinely require it.

Which model should we use?

It depends on the task, your accuracy needs, cost, and data sensitivity. Evertech tests candidate models against your real examples rather than guessing, and keeps the integration model-agnostic where possible, so you can switch as prices and capabilities change. We recommend based on evidence, not on whichever model is currently in fashion.

How do you know the output is good enough?

Evaluation. Evertech builds a test set from your real cases and scores model output against it, so quality is a number you can track, not a feeling from a demo. We set the accuracy bar with you up front, and guardrails catch the failures that slip through before users see them.

Can this keep our data private?

Yes. Evertech designs the integration around your data-handling rules: which models may see what, where data is processed, and what is retained. We can use providers with strong privacy terms or self-hosted models where sensitivity demands it. Data boundaries are a design decision we make with you, not an afterthought.

Related services

Pairs well with

AI agents & assistants

Evertech builds AI agents and in-app assistants that complete real tasks by calling your tools, with human review at the points that matter

Data & retrieval pipelines

Evertech builds vector search and RAG pipelines that turn your knowledge base into precise, cited answers your teams and agents can trust

Let's find what's worth building

A short discovery call to understand your business and show you where software and AI would pay off first.